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How to serve a Delta-rule model with compressed recurrent state with STEP Quant

Run a Delta-rule model under SGLang with STEP Quant state compression to cut per-request state memory.

  • Research
  • Advanced
  • half day
  • From the README
  • Any agent

Who it is forAn inference engineer serving a Delta-rule model under concurrent requests, where fixed-size state still adds up in memory.

The problemRecurrent state memory grows with concurrency, and uniform low-bit quantization adds errors to every state update.

The outcomeAn OpenAI-style local endpoint, served from SGLang, that stores recurrent state in the STEP Quant 6-bit format.

What you need first

  • Model checkpoints for a supported model, such as Qwen3.8-27B, placed on disk
  • A CUDA toolkit and NVIDIA GPUs (the Qwen example uses four GPUs; the paper used NVIDIA A800 GPUs)
  • Python with the tested stack: SGLang 0.5.12, PyTorch 2.11.0, Triton 3.7.1
  • A clone of the repository, run from its root

Steps

  1. Run bash scripts/reuse_sglang_env.sh /path/to/sglang-env and bash scripts/reuse_calibration_env.sh /path/to/qwen-env qwen to prepare the environments.
  2. Run .venv-eval/bin/python -m stepquant.reproduction --models qwen --formats stepquant6 --devices 0,1,2,3 to produce the calibrated plan at artifacts/reproduction/plans/qwen-stepquant6.pt. (inferred, check the README)
  3. Run CUDA_VISIBLE_DEVICES=0,1,2,3 .venv-serving/bin/python -m stepquant.sglang --model-path /path/to/Qwen3.8-27B --tp-size 4 --state-format stepquant6 --plan artifacts/reproduction/plans/qwen-stepquant6.pt --attention-backend triton --served-model-name stepquant --host 127.0.0.1 --port 31080. (inferred, check the README)
  4. Send a test request to the server on 127.0.0.1 port 31080 and confirm it returns a completion.

Prompt for your agent

Using the STEPQuant repo at <repo_path>, calibrate a stepquant6 plan for the model at <model_path> and start the SGLang server with --state-format stepquant6. Report the server URL and the text of one test completion, and verify the server log shows the stepquant6 state format was loaded.

Caveat. The README's serving stack is pinned to SGLang 0.5.12, PyTorch 2.11.0, and Triton 3.7.1, and the example needs four GPUs, so other setups are not covered by the README.

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